Search Results (484)

Copper dithiocarbamate complexes have long been known and are relevant in biology, medicine and material science; however, their low solubility in water can be a limitation. Therefore, the search for modified ligands is an important task. Copper complexes with five phosphorylated dithiocarbamates were investigated in aqueous solutions by several experimental and theoretical methods. Copper(II) bis-complex formation constants were obtained from spectrophotometric titrations. Based on UV-vis and EPR spectroscopy data, the presence of monoligand complexes (in excess copper) and hydroxy-forms (under basic conditions) was revealed. The structures of the obtained forms were optimized using DFT calculations. The instability of complexes under neutral and acidic conditions was established and interpreted by the dimerization upon protonation. This assumption is supported by association constants derived from quantum chemically computed Gibbs free energies for protonated and non-protonated copper(II) bis-dithiocarbamate complexes. Crystal structures of protonated binuclear and non-protonated mononuclear complexes were established using X-ray diffraction. The redox properties of the complexes were studied by cyclic voltammetry; the electrochemical behavior of the complexes was strongly influenced by pH. The scheme of the copper(I)/(II)/(III) species transformations, including chemical and electrochemical stages, is proposed on the base of experimental data and quantum-chemical calculation results. Full article

Background and Objectives: Tooth impaction is a common developmental dental anomaly characterized by the failure of eruption within the expected physiological timeframe. Permanent canines represent the second most frequently impacted teeth after third molars and may lead to functional, esthetic, and orthodontic complications. This systematic review aimed to synthesize current evidence regarding the incidence, anatomical distribution, etiological determinants, and diagnostic evaluation of permanent canine impaction. Materials and Methods: A systematic literature search was conducted in PubMed, PubMed Central, and ScienceDirect for studies published between December 2009 and December 2025. Studies reporting prevalence data, anatomical positioning, etiological factors, or imaging characteristics of permanent canine impaction were included. Study selection followed PRISMA 2020 guidelines, and 31 studies were included in the qualitative synthesis. Two independent reviewers screened titles, abstracts, and full texts. Methodological quality was assessed using the Joanna Briggs Institute Critical Appraisal Tools. Results: Thirty-one studies met the inclusion criteria and were included in the qualitative synthesis. The reported prevalence of maxillary canine impaction ranged from 0.97% to 7.10%, while mandibular impaction occurred less frequently. Palatal displacement represented the most common positional pattern. Major etiological factors included retained deciduous canines, dental arch constriction, supernumerary teeth, odontomas, and genetic anomalies such as lateral incisor agenesis. Cone-Beam Computed Tomography (CBCT) demonstrated superior diagnostic accuracy compared with panoramic radiography. Conclusions: Permanent canine impaction is a multifactorial condition predominantly influenced by local anatomical and environmental factors, with genetic predisposition acting as a secondary contributor. Early diagnosis and appropriate imaging assessment are essential to prevent complications such as root resorption and to optimize treatment outcomes. Full article

Construction and demolition waste (CDW) represents a critical environmental challenge in the building sector, with global generation exceeding 3.57 billion tonnes annually. The circular economy (CE) framework offers a transformative pathway through selective deconstruction and material recovery, yet implementation faces significant barriers including information asymmetry, supply chain fragmentation, and regulatory uncertainty. This study conducts a systematic literature review using the Context–Mechanism–Outcome (CMO) framework to analyze how computational methods, specifically Digital Twins (DT), Building Information Modeling (BIM), Internet of Things (IoT), blockchain, artificial intelligence, and robotics, act as enablers for resilience in CDW management. Following PRISMA 2020 guidelines and realist synthesis principles, we analyzed 42 high-quality empirical studies from Web of Science and Scopus (2015–2025). Our analysis identifies seven primary mechanisms: traceability (M1), simulation (M2), classification (M3), tracking (M4), collaboration (M5), analytics (M6) and robotics (M7). These mechanisms interact with four critical contexts (information asymmetry, supply chain fragmentation, economic uncertainty, operational risks) to generate outcomes at two levels: resilience capabilities (visibility, monitoring, collaboration, flexibility, anticipation) and performance indicators (recovery rates, cost reduction, CO₂ emissions mitigation, occupational safety). Key findings from the CMO analysis reveal that blockchain-enabled traceability increases material recovery rates by 15–25%, DT simulation reduces deconstruction costs by 20–30%, and computer vision automation improves sorting accuracy to 85–95%. The study contributes middle-range theories explaining how digital technologies enable circular transitions under specific contextual conditions, offering actionable strategic implications for researchers, project managers, technology developers, and policymakers committed to advancing computational economics in sustainable construction. Full article

The photocycloaddition (PCA) of chalcones represents an important reaction pathway for accessing substituted cyclobutanes, which is a molecular framework with utility in synthetic chemistry, materials science, and medicine. In the past, our group has demonstrated the utility of the large cavity of γ-CD as a container for encapsulating two photo reactants for directing the PCA of several classes of aryl alkenes with high stereo- and regioselectivity: the cavitand-mediated photodimerization (CMP) approach. The CMP of chalcones reported in this work further demonstrates the effectiveness of this approach as high yields of dimers were observed in the photoreactions, while they were non-reactive in the solid state and yielded only the isomerization product in homogeneous media. The γ-CD CMP of chalcones yielded predominantly dimerized products in very good to high yields (>70%), composed of a mixture of three dimers in different proportions with syn HH as the major product. Computational analysis of the ground state complex structures revealed a strong correlation between the stability of the complex and predominance of the stereoisomer in the mixture. Further insights were deduced from temperature-dependence studies, which showed a shift in dimer selectivity tending towards a single stereoisomer. Full article

Auditory Brain–Computer Interfaces (BCIs) constitute the vital intervention for profound sensorineural hearing loss where the auditory nerve is compromised, yet their clinical efficacy remains restricted by substantial biological bottlenecks and limited spectral resolution. This review critically examines the evolutionary paradigm of auditory restoration, tracing the transition from static physical replacement to dynamic biological symbiosis. We systematically analyze physiological barriers across cochlear, brainstem, and cortical levels, elucidating how rigid interfaces provoke chronic tissue responses and why linear encoding protocols fail in distorted central tonotopy. The article synthesizes emerging methodologies in material science, demonstrating how soft, bio-integrated electronics and biomimetic topologies effectively address mechanical impedance mismatches. Furthermore, the trajectory of neural encoding is evaluated, highlighting the paradigm shift from traditional envelope extraction to deep learning-driven non-linear mapping and adaptive closed-loop neuromodulation. Finally, the potential of high-resolution modulation techniques, including optogenetics and sonogenetics, alongside AI-facilitated intent perception for active listening, is assessed. It is concluded that future neuroprostheses must evolve into symbiotic systems capable of seamlessly integrating with neural plasticity to enable high-fidelity cognitive reconstruction. Full article

For decades, zirconia- and ceramic-based materials have dominated dental crown fabrication due to their durability and aesthetic appeal. However, a fundamental shift is occurring as polymeric alternatives emerge with notable advantages: better adhesive bonding, versatile aesthetics, lower costs, and a lighter weight. The advances in polymer chemistry and additive manufacturing have significantly impacted prosthodontics, allowing the rapid creation of highly customized, patient-specific restorations with a precision previously impossible (achieved through advanced Computer-Aided Design software and standardized 3D-printing equipment) with traditional methods. This review provides a detailed analysis of 3D-printed polymeric dental crowns from various angles. It explores the materials science behind different polymers, compares manufacturing methods, and evaluates the mechanical performance and biocompatibility. Despite the progress, polymeric materials still fall short of matching the mechanical properties of advanced ceramics, especially in compressive strength and wear resistance. Moreover, there is limited long-term clinical data over five to ten years. The lack of standardized testing protocols complicates cross-study comparisons, and the regulatory pathways for patient-specific 3D-printed devices are still developing, creating uncertainty for manufacturers and clinicians. The future prospective looks promising in many ways such as innovations like four-dimensional printing, where materials respond dynamically to environmental stimuli, which could enable crowns that adapt to changing oral conditions. Nanocomposites with functionalized nanoparticles might enhance mechanical properties while maintaining printability. AI-driven design optimization could automate and improve the crown morphology, occlusal contacts, and fit. Incorporating bioactive materials could turn crowns into active therapeutic devices that promote remineralization and combat bacterial colonization. This review summarizes the current knowledge, highlights the key gaps, and suggests steps toward establishing polymeric 3D-printed crowns as viable long-term alternatives capable of competing with or surpassing traditional ceramic options. Full article

Perceived photorealism in architectural 3D rendering is not determined solely by physical accuracy or rendering complexity but also by a limited set of visual cues that observers rely on when judging realism. This literature review synthesizes findings from 41 peer-reviewed studies spanning perception science, computer graphics, and immersive visualization, with the aim of identifying the cues that most strongly contribute to perceived photorealism in rendered scenes. Convergent evidence from psychophysical experiments, user studies in virtual and augmented reality, and rendering-oriented analyses indicate that six cue categories consistently dominate realism judgments. Across the reviewed literature, realism judgments depend less on scene complexity or the number of visual elements and more on the consistency and plausibility of these cues for supporting inferences about shape, material, and spatial layout. These findings suggest that photorealism emerges from the alignment of the rendered image structure with perceptual expectations learned from real-world visual experience. The implications for architectural visualization workflows and directions for future research on cue interactions and perceptual thresholds are discussed. Full article

Erosion is widely encountered in circulating fluidized bed (CFB) boilers. Investigations into erosion mechanisms and mitigation strategies are essential for improving the operational reliability and reducing economic losses. This paper presents a bibliometric analysis and review of recent progress in erosion-related studies for CFB boilers, identifying three main research hotspots: CFD-based erosion prediction from flow dynamics, anti-wear coatings from materials science that consider chemical corrosion, and boiler design adaptations for biomass. Building upon classical studies on solid particle erosion mechanisms and accounting for the high-temperature and reactive chemical environments characteristic of CFB boilers, the erosion mechanisms in CFB boilers are systematically summarized. It is revealed that particle flow parameters dominate the erosion process, coupled with chemical corrosion. Subsequently, the application of computational fluid dynamics (CFD) methods to erosion prediction and mitigation in CFB boilers is reviewed, and the characteristics of various anti-wear techniques are discussed. It is found that CFD can serve as an effective tool for the design of anti-wear techniques; however, the design must account not only for erosion resistance but also for the resulting impacts on boiler heat transfer and thermal inertia. Finally, perspectives and future research directions for erosion studies in CFB boilers are outlined. Full article

Nuclear Magnetic Resonance (NMR) spectroscopy has long been a cornerstone in the structural elucidation of molecules, offering unique insights into atomic-level connectivity, conformation, and dynamics. Over the past decades, methodological and technological advances have significantly expanded its capabilities and applications. This manuscript charts the evolution of NMR from classical 1D/2D experiments to modern methods empowered by ultrahigh magnetic fields, cryogenic probes, non-uniform sampling, new methodologies, and hyperpolarization. We emphasize the growing synergy between experiment and computation, where automated analysis, quantum chemical calculations, and machine learning are dramatically enhancing the accuracy and efficiency of structure determination. We also highlight NMR’s broadening scope in areas ranging from complex mixtures and natural products to biomolecular and materials science. Full article

The convergence of Robotics and artificial intelligence (AI) has transformed engineering by enabling the design of intelligent systems capable of learning, adapting, and performing complex tasks. These synergies are driving innovation across multiple engineering disciplines, including mechanical, materials, electrical, industrial, civil, and aerospace engineering. This review provides a comprehensive overview of the knowledge structure and emerging research directions of Robotics and AI in engineering, with the aim of identifying research trends, influential authors, leading institutions, and emerging thematic areas. Data were collected from the Web of Science and Scopus databases, covering the period from 2020 to 2025, and analyzed using bibliometric mapping techniques and performance indicators. The results reveal a sustained growth in research on autonomous systems, collaborative robots, and human–robot interaction within engineering contexts, with a strong emphasis on AI-driven optimization. Bibliometric analyses show that deep learning, reinforcement learning, and computer vision constitute the core enabling technologies structuring the field. In addition, the results highlight a high degree of international collaboration and a concentration of scientific output and impact in a limited number of leading countries, institutions, and journals. Full article

Laser-based manufacturing processes—including marking, hardening, cutting, and welding—demand the precise selection of processing parameters, as the resulting surface state is critically dependent on the delivered power density and beam–material interaction time. This study presents a unified predictive framework for estimating the critical surface power density thresholds for melting q_scm and evaporation q_scv as functions of scanning speed v for the following four technologically important metallic materials: titanium, C26000 brass, SS304 stainless steel, and 42CrMo4 alloy steel. The principal novelty of this work is twofold. First, it provides the first directly comparative analysis of these four materials under identical, standardized laser conditions (λ = 1064 nm, d = 40 μm, constant absorptivity A = 0.4), eliminating the confounding effects of variable beam geometries and optical assumptions that hinder cross-study comparisons. Second, it translates fundamental thermophysical principles into a practical engineering tool, such as a validated spreadsheet calculator that outputs material-specific threshold curves in real time, enabling rapid, physics-based parameter estimation without recourse to complex numerical simulations. The computed threshold curves exhibit a consistent non-linear increase with scanning speed for all materials, governed by the inverse relationship between interaction time and required power density. The following clear material hierarchy emerges: C26000 brass exhibits the highest thresholds (e.g., q_scm = 0.94 × 10¹⁰ W/m², q_scv = 10.74 × 10¹⁰ W/m² at v = 100 mm/s) due to its high thermal conductivity, while titanium shows the lowest (q_scm = 0.19 × 10¹⁰ W/m², q_scv = 0.48 × 10¹⁰ W/m² at v = 100 mm/s) as a consequence of strong heat confinement. SS304 and 42CrMo4 occupy intermediate positions, with 42CrMo4 demonstrating notably higher evaporation resistance than SS304 despite similar melting thresholds. The resulting dual-threshold framework delineates three distinct process regimes—sub-melting heating, melting-dominant processing, and evaporation—providing a quantitative basis for parameter selection in applications ranging from surface hardening to micromachining. By bridging the gap between theoretical material science and applied manufacturing, this work offers a robust, first-order reference for process design and establishes a methodological template for future comparative studies of laser–material interactions. Full article

Elucidating the complex structure–property–performance relationships in multifunctional polymer matrix composites (PMCs) remains a formidable challenge. This difficulty stems from the intricate coupling between formulation variables, porous morphology, physicochemical attributes, and functional outcomes, particularly under the “small-data” constraints inherent to experimental materials research. This study introduces a robust, interpretable machine learning (ML) framework tailored for the analysis of macroporous polyHIPE-based magnetic composites. All analyses were conducted exclusively on curated experimental data reported in the literature. By leveraging a curated dataset synthesized from multiple independent studies with harmonized characterization protocols, we integrated processing parameters and quantitative morphological descriptors to predict two critical engineering outputs: dye removal efficiency (%) and saturation magnetization (M_s). Nonlinear ensemble ML models were rigorously trained and evaluated using repeated cross-validation and cross-study validation strategies to ensure predictive robustness and domain transferability. The superior performance of nonlinear models over linear baselines underscores that composite functionality is governed by synergistic, non-additive interactions. Model-agnostic interpretability analyses further revealed that pore interconnectivity and accessible surface area are the primary determinants of adsorption performance. Conversely, while increased magnetic nanoparticle loading enhances magnetic responsiveness, it induces a significant trade-off with adsorption efficiency. These findings demonstrate that uncertainty-aware ML can extract generalizable, physically grounded design insights from heterogeneous experimental literature, providing a streamlined, data-driven pathway for the rational design and screening of multifunctional porous materials without necessitating additional experimental overhead. Full article

Filamentous fungi are increasingly recognized as versatile biological platforms for the development of advanced (bio)sensing technologies, owing to their extensive secretory capacity, material-forming ability, and intrinsic bioelectrical activity. This review critically surveys recent progress in fungal-based sensing within a multiscale framework spanning molecular, material, computational, and ecological domains, with particular emphasis on developments reported over the past five years. Key advances involving secretome-derived biomolecules, mycogenic nanomaterials, mycelium-based living materials, and fungal electrophysiology are discussed alongside emerging approaches for environmental monitoring that integrate sensor networks, imaging platforms, and data-driven analytics. Collectively, these works demonstrate that fungal systems can enhance biosensor sensitivity, selectivity, and sustainability, while enabling unconventional paradigms of signal transduction, material-integrated sensing, and biologically mediated computation. At larger spatial and temporal scales, mycelial growth dynamics and electrical activity provide measurable responses to mechanical, chemical, and environmental perturbations, supporting early applications in wearable devices, structural materials, and ecosystem monitoring. Despite significant progress, challenges remain in reproducibility, long-term stability, mechanistic understanding, and scalable device integration. Overall, the evidence reviewed highlights filamentous fungi as biologically adaptive and ecologically embedded systems with substantial potential to support next-generation (bio)sensing technologies, while underscoring the need for integrative approaches that combine biological insight with materials science, electronics, and artificial intelligence. Full article

Droplet-based microfluidics has emerged as a powerful platform for precise fluid manipulation in biomedical, chemical, and material science applications. Among various geometries, T-junction microchannels are widely utilized for droplet generation and splitting due to their simplicity and reliability. This review provides a comprehensive overview of droplet splitting mechanisms in T-junction microfluidic systems, with particular emphasis on the role of actuation methods in enhancing control and functionality. We first discuss the fundamental physics governing droplet behavior, including the influence of capillary and viscous forces, flow regimes, and geometric parameters. Passive strategies based on flow rate tuning and channel design are outlined, followed by an in-depth examination of active actuation techniques: thermal, electrical, magnetic, acoustic, and pneumatic and their effects on droplet dynamics. In addition, the review highlights computational modeling approaches and experimental tools used to characterize and predict splitting behavior. Finally, we explore the current challenges and future directions in integrating multifunctional actuation systems for real-time, programmable droplet control in lab-on-a-chip platforms. This article serves as a foundational resource for researchers aiming to advance microfluidic droplet manipulation through actuator-enabled strategies. Full article

Objectives: To compare artificial intelligence (AI) crown design with expert or non-AI computer-aided (CAD) design for single-unit tooth and implant-supported crowns across efficiency, marginal and internal fit, morphology and occlusion, and mechanical performance. Materials and Methods: This systematic review was conducted and reported in accordance with PRISMA 2020. PubMed MEDLINE, Scopus, Web of Science, IEEE Xplore, and Dentistry and Oral Sciences Source were searched from 2016 to 2025 with citation chasing. Eligible studies directly contrasted artificial intelligence-generated or artificial intelligence-assisted crown designs with human design in clinical, ex vivo, or in silico settings. Primary outcomes were design time, marginal and internal fit, morphology and occlusion, and mechanical performance. Risk of bias was assessed with ROBINS-I for non-randomized clinical studies, QUIN for bench studies, and PROBAST + AI for computational investigations, with TRIPOD + AI items mapped descriptively. Given heterogeneity in settings and endpoints, a narrative synthesis was used. Results: A total of 14 studies met inclusion criteria, including a clinical patient study, multiple ex vivo experiments, and in silico evaluations. Artificial intelligence design reduced design time by between 40% and 90% relative to expert computer-aided design or manual workflows. Marginal and internal fit for artificial intelligence and human designs were statistically equivalent in multiple comparisons. Mechanical performance matched technician designs in load-to-fracture testing, and modeling indicated stress distributions similar to natural teeth. Overall risk of bias was judged as some concerns across tiers. Conclusions: Artificial intelligence crown design delivers efficiency gains while showing short-term technical comparability across fit, morphology, occlusion, and strength for single-unit crowns in predominantly bench and in silico evidence, with limited patient-level feasibility data. Prospective clinical trials with standardized, preregistered endpoints are needed to confirm durability, generalizability, and patient-relevant outcomes, and to establish whether short-term technical advantages translate into clinical benefit. Full article